How to make the most of local explanations: effective clustering based on influences
Abstract
Machine Learning is now commonly used to model complex phenomena, providing robust predictions and data exploration analysis. However, the lack of explanations for predictions leads to a black box effect which the domain called Explainability (XAI) attempts to overcome. In particular, XAI local attribution methods quantify the contribution of each attribute on each instance prediction, named influences. This type of explanation is the most precise as it focuses on each instance of the dataset and allows the detection of individual differences. Moreover, all local explanations can be aggregated to get further analysis of the underlying data. In this context, influences can be seen as new data space to understand and reveal complex data patterns. We then hypothesise that influences obtained through ML modelling are more informative than the original raw data, particularly in identifying homogeneous groups. The most efficient way to identify such groups is to consider a clustering approach. We thus compare clusters based on raw data against those based on influences (computed through several XAI local attribution methods). Our results indicate that clusters based on influences perform better than those based on raw data, even with low-accuracy models.
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